Scattered in different places — we call it the Newton Factor. Noēsis answers across your video, audio, documents and tables, with the source attached. On your hardware.
Zero confabulations on factual data — 85 measured runs on a cold stack, including a 2-billion-parameter model. When the answer is not there, it says so.
Noēsis finds what you didn't know you were looking for.
The History
Noēsis begins with an argument — the most valuable knowledge in any organisation is the connection between things that never talk to each other. It was made in 1666. It ends where your knowledge lives, in whatever form you saved it.
Anno 1666 · Woolsthorpe
Afalling apple connected two worlds. The same law that pulls an apple to the
ground pulls the Moon around the Earth — and the Earth around the Sun. What
seemed two separate domains proved to be one system, and that connection
became the foundation of modern physics.
Afalling apple connected two worlds — the same law that pulls the apple to
the ground pulls the Moon around the Earth. Many domains, one system.
❦
Noēsis does the same for your knowledge.
The insight is already in your documents — in no single one of them, spread
across the ones that never talk to each other. What was missing was never
more data. It was the connection.
Noēsis does the same for your knowledge. The insight is already in your documents. What was missing was never more data. It was the connection.
Noēsisknowledge that thinks
Quattuor studia · drawn with the same nib
Many domains, one system.
The argument this page rests on.
Newton found one law where everyone else saw separate worlds — the move we call the Newton Factor. We read your knowledge the same way: however many departments, one answer.
Nothing in one source.
Everything between them.
The tape with half the answer and the file with the other half were never read together. Noēsis answers across them and lists every one.
Every answer with its source.
Position included.
An answer is worth nothing if you cannot check it. Each one arrives with the source it came from, and the place inside it — the second, the page, the line.
When the archive is silent, so are we.
The refusal is the feature.
If your material does not hold the answer, you are told so. Nothing is invented to fill the gap - and the gap is the useful part.
…and the idea, told in 75 seconds.
INTRODUCING NOĒSIS · 75s · WITH SOUND
Press play, turn it up. 75 seconds on the idea at the heart of Noēsis: your next breakthrough is already in your data — waiting to be connected.
What is Noēsis?
The reasoning layer for everything you hold, in four moves.
Noēsis is a deterministic-first Knowledge Engine — a category of one. What it is not: a technique. Graph RAG is a set of components a team wires together, the way “microservices” is an architecture, not a product. What it is: the finished system — it extracts the graph, routes across domains, grounds every answer in its source and learns from usage, one product on your infrastructure, nothing to assemble. Everyone else bolts determinism onto a probabilistic pipeline as a feature; here it is the starting point. Facts first, invention never: when your material does not hold the answer, Noēsis says so.
01
It reads the way you read.
One pass is not reading. Noēsis finishes the source before it answers, so what only a second reading would reveal is already in the answer.
02
It finds what you didn't know you were looking for.
Answers that live in no single source, across knowledge bases that were never merged. Finding the right one costs no model call at all.
03
It answers the way you would want a fact answered.
Numbers, dates and names arrive with their source and their position in it. When the archive does not hold something, Noēsis says so instead of filling the gap.
04
It gets faster where you use it.
The routes your organisation actually needs become the quickest ones: the hundredth question is faster and more precise than the first. No labels, no retraining, no re-indexing project.
The product, in action
Three short videos — real questions, real content, answers with their sources. No slides.
The Newton Factor, made executable: every system stays exactly where it is, and the connections between them answer when you ask — nothing merged, nothing copied.
NOĒSIS
Mesh
Archive
what exists
Audience
who watches
Rights
where & when
Matchday
what happens
Marketing
Social
Talent
…
…and every system you run.
Systems that were never connected, answering as one — routed before the model is ever asked.
Field-tested · IBC 2026
Prepared, not generic
Every deck we build starts with them, not with us. Page one is their value — their archive, their rights, their audience. Page two is the solution. We send them to the people they were written for; we don't publish them with someone else's name on the front.
A league
When a CCO asked for a deck
We didn't send a product overview. We built a two-page deck about his league — the archive, the rights, the schedule, the audience — and the reasoning layer that answers across all four. That deck is what got us the meeting.
A broadcaster
Answered live on the show floor
We ran Noēsis live at IBC 2026 for a European public broadcaster's research team: a question in plain language, an answer in seconds, across six knowledge bases — five hops, five bridges, thirteen sources. The corpus was ours, built from public broadcast material; theirs is seven decades of radio and television, and the conversation since has been about what that becomes once it can be asked a question — gaps declared, not filled in.
The first deterministic-first Knowledge Engine does what a wired-together pipeline cannot — and names its proof as it goes.
✕ Without Noēsis
✕ Keyword search
✕ Siloed data sources
✕ Manual cross-referencing
✕ Cloud dependency
4 systems
where a typical answer actually lives — archive, rights, schedule, audience
✓ With Noēsis
✓ Semantic graph reasoning
✓ Connected domains
✓ Automatic multi-hop traversal
✓ 100% on-premises
7 seconds
cross-domain answer delivery
Deterministic precision on facts, where the others drift
Numbers, dates and names come back exact, with their source and their position in it. On multi-hop question answering it scores +27.8 points over Microsoft's GraphRAG — 59.5 EM against 31.7 for Microsoft's GraphRAG, both answering with GPT-4o.
A straight line to the source
Every answer carries the timestamp, page or line it came from. What the archive cannot support, it declines — nothing is invented to fill the gap, and the gap is the useful part.
Completely on your own hardware. No cloud, ever
It runs on consumer hardware — from an integrated GPU down to CPU-only — not an enterprise cluster. No data leaves your network: your hardware, your models, your material.
Any source of knowledge you hold
Video, audio, documents, spreadsheets and catalogues are read once and answered together. A new kind of source plugs in without rebuilding the system around it.
Archive-scale work that does not fall over
It processes under heavy load in the background, adapting its own pace to the hardware instead of crashing it — and when a run is interrupted, it resumes exactly where it stopped, never redoing finished work.
Your archive stays where it is
Thousands of gigabytes of video need not move. Noēsis ingests on demand — only the material you point it at, pulled in, processed and discarded — leaving the archive untouched.
How it thinks
Three steps from wherever your knowledge lives, in any format, to deep cross-domain reasoning.
Step 01
Drop anything in
PDFs, code, video, audio — drop them in. The system detects format and domain automatically.
Step 02
The graph builds itself
Progressive learning extracts entities, relationships, and semantic structure. Then reviews to find connections it missed.
Step 03
Ask across every domain
Ask anything. Mesh routes to the right knowledge bases and fuses multi-hop answers in <2ms.
Everything you already hold knows more than you realise.
Learns.Connects.Reveals.
"The connection existed in no single source. The graph found it; the model only had to say it."
Built for every knowledge-based industry
One engine, every room that runs on what it knows: healthcare, legal, research, engineering, product, journalism, media and sport. Where we have proof we name it — the engine was not built for one of them.
Healthcare & Life Sciences
Connect clinical research, protocols and regulatory correspondence. Surface the trial note that qualifies a protocol — and the place where two sources disagree.
Legal & Compliance
Map relationships between contracts, regulations, and case precedents. Surface clauses that interact across different agreements.
Research & Academia
Build a living literature review that grows with every paper. Find cross-disciplinary connections that inform novel hypotheses.
Fashion & Luxury
Connect design archives, trend research, supplier specs and campaign content. Surface the 1997 pattern study that matches this season's direction — before the trend reports do.
Product & Strategy
Connect customer interviews, market research, technical specs, and meeting notes. See how a user need maps to a technical constraint across dozens of sources.
Software & Technology
Your code becomes queryable knowledge. No more engineers leaving without handover. BDNA turns your codebase into a connected graph — linked to your docs, requirements, and architecture.
Media & Broadcasting
Link stories, sources, interviews, and archive footage across years of production. Surface connections between emerging stories and past reporting.
Video & Audio Intelligence
Everything you have is a source.
A board meeting is not a document. Neither is a 400-page tender, a batch record, a claim file, a season of rushes or the recording of the call where the decision was made — and the answer someone in your building needs usually lives between three of them. Noēsis reads each in its own format and gives each its own coordinates: the second of a sentence, the cell of a sheet, the line of a page. A format we have not met before is a transformer we write; the layer is built for that.
Two hours of recording become searchable in minutes. Cross-reference what was said with what was written: the site walkthrough against the spec, the consultation against the protocol, the interview against the contract, the match against the rights sheet.
Connect your data — wherever it lives
No migration, no data copying, no cloud dependency. One command syncs your sources into a connected knowledge graph.
SharePoint
Microsoft 365 document libraries. Delta sync — only pulls what changed. OAuth2 authentication.
Confluence
Atlassian spaces and pages. Exports as structured markdown. Labels and attachment support.
In build now: Google Drive, Notion, Databricks, ServiceNow. Same interface, one CLI for all sources. Tell us which one you need first and it moves up the queue.
Noēsis speaks the language of AI agents. Connect your favorite tool and let it query your knowledge graph.
Pi Agent
Native extension with full graph traversal, semantic search, and context retrieval. Your agent queries the knowledge graph and explores cross-document connections.
noesis-pi
Claude Code
Drop-in plugin for Claude Code. Anthropic's coding agent gains instant access to your knowledge graph — query nodes, search embeddings, retrieve context.
noesis-claude-code
Gemini CLI
Google's Gemini CLI extension. Semantic search and graph traversal directly from the terminal. Perfect for developers who live in the command line.
noesis-gemini-cli
Cursor IDE
Integrate Noēsis into Cursor IDE. Your code editor becomes a knowledge-aware workspace — query your graph without leaving your coding session.
noesis-cursor
Universal adapter: npx -y noesis-mcp-server — works with any MCP-compatible agent (Continue, Cline, Roo Code, Kiro, and more)
The non-negotiables
Sovereignty, speed and security are not features here — they're the floor. Patent-pending architecture, every choice deliberate.
Privacy first
Run everything locally. No cloud dependencies. Ideal for healthcare, government, military, and legal domains where data sovereignty is non-negotiable.
Keeps pace with whatever you add
Proprietary parallel engine with adaptive concurrency: it ingests, extracts and indexes in one pass, and scales to whatever hardware you give it. No queues, no babysitting.
Runs on your hardware
No data center required. Frontier-class reasoning on the machines you already own. Your infrastructure, your rules.
Cloud or local
Need brute force? Plug in any cloud LLM. Want zero latency and full privacy? Run locally. Same API, same results.
Small models, big reasoning
The graph does the heavy lifting: compact local models answer like frontier ones, because the intelligence lives in your knowledge — not in parameter count.
Secure by design
Nothing you feed it can instruct it. Every source is processed as data — never as instructions. Self-healing pipeline with zero data loss.
Healthcare. Legal. Research. Engineering. Media. Your data stays yours. Your knowledge works harder.
The numbers
+27.8 points over Microsoft's GraphRAG on HotpotQA: the standard test for multi-step reasoning, 1,000 questions whose answer lives in no single document. Answered by a model running locally, no cloud.
Noēsis (35B on-prem) + GPT-4oGraph built locally, zero cloud
59.5
Noēsis (35B on-prem) + 2.3B answerBoth local, zero cloud
On factual questions, Noēsis answers with the exact source in hand — or says the archive doesn't hold it. Values are computed before the model speaks, so there is nothing to invent: across 85 measured runs on a cold stack — including a 2-billion-parameter model — no value was ever derived, altered, or attributed to the wrong source. Certified on the HotpotQA benchmark and on real corpora across industries: a broadcaster's archive, a league's footage, a federation's public pages. What the archive cannot answer is refused, never invented.
The ledger
Every statement on this page that can be checked has a row here: what we state, where it came from, when, and how you verify it.
What we stateWhere it came fromWhenCheck
01+27.8 points over Microsoft's GraphRAG on HotpotQA — 1,000 questions whose answer lives in no single document.Benchmark study, peer-validated results.Aug 2026arXiv 2608.15919
0259.5 EM against 31.7 for Microsoft's GraphRAG — both answering with GPT-4o. With the answer model replaced by a 2.3B running locally: 47.2 EM, still above GraphRAG.Same study, ablation table. The graph is built on-premises by a 35B model in every run.Aug 2026arXiv 2608.15919
03No value derived, altered or attributed to the wrong source across 85 measured runs on a cold stack, including a 2-billion-parameter model.Internal measurement log, factual questions on real corpora.Jul–Sep 2026Under NDA
04Factuality-critical querying on small local models, described as principles — the implementation is not published.Second study: deterministic-first retrieval, two-tier context hydration.Sep 2026arXiv 2609.07663
05Answers held in no single source of yours — not ours. One week, your corpus, your machines.A PoC we build for you: the corpus you choose, on your machines.On requestYour PoC
06Invention patent application filed with the Italian patent office, covering the extraction and cross-knowledge-base routing.App. No. 102026000023146 — the only application number printed on this site.5 Aug 2026Public number
A row we cannot prove is not a row. Where the check is not public we say so in the open column, and we would rather show you an empty cell than an adjective.
How we price
One flat price per deployment. Not per person, not per document, not per query. Noēsis runs entirely on your machines, so nothing you do inside it can move the invoice.
A deployment is one organisation: its knowledge bases, its environments, all of them connected. A separate legal entity is a separate deployment — and that is the only unit we count.
Flat — what is inside
Everything the product does · unlimited use
Extraction, the graph, the Mesh across your knowledge bases, the modalities you license, updates. Ten users or ten thousand, one terabyte or a hundred, one query a day or a million: the same number, because our cost does not move when your usage does. That is the one thing a hosted vendor cannot offer you.
Development
Quoted as work · never as a surprise
A connector we have not built, a model you want tuned, an integration with a system we have not met. Scoped and priced before anyone opens a laptop, at a day rate written in the order.
Assistance
Response times, people · a retainer
First-line support and updates live inside the flat price. What we sell is certainty: agreed response times, a named engineer, someone in the room on the day you go live.
The same number in year two and year three, written down. The only thing that changes it is new scope, agreed before we do it.
Ask for a quote · Giving Noēsis to users who are not your staff, or putting it inside a product you sell?
That is a different model, ask us
The Architecture
Six modular components. Each independent, replaceable, and specialized.
Neuron
Knowledge extraction engine. Progressive reading, entity discovery, relationship mapping through deep analysis.
Cortex
AI inference engine. Runs LLMs on consumer hardware with domain-adaptive optimization — legal, medical, engineering.
Mesh
Cross-domain intelligence. Routes queries, discovers hidden connections between separate domains at runtime.
Code intelligence engine. Turns source code into queryable knowledge. Syncs with Mesh for cross-domain discovery.
Domain-Adaptive Intelligence
The machine you already own goes further than you think. Noēsis takes what a job demands of the hardware and handles the rest, within the VRAM you already have.
Legal corpora, match footage, engineering specs, clinical notes: each gets the machine it needs, and nobody has to configure it. The model you validated stays the model you run.
Bring one question you actually have.
We build a custom demo of Noēsis for you: your question, the corpus you choose, and Noēsis answering it while you watch. That is the demo.
One question, and where it lives. The formats you hold today are enough: a drive, a MAM, a SharePoint, a folder of PDFs.
A demo built for you. Your own material, or public material if you would rather start there — you choose what we work from, and Noēsis runs on your machines either way.
One call, forty-five minutes. The answer, your corpus, and what a pilot would need from either side.
